Joerg Marks
2 years ago
1 changed files with 180 additions and 0 deletions
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{ |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "8ab45695", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"#\n", |
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"# An example of the minimzer usage in tensor flow\n", |
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"# the loss function is plotted and the result in terms of a line\n", |
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"#" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "270932f3", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import numpy as np\n", |
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"import matplotlib.pyplot as plt\n", |
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"import tensorflow as tf" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "77cd99a8", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Define the training data\n", |
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"train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,\n", |
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" 7.042,10.791,5.313,7.997,5.654,9.27,3.1])\n", |
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"train_Y = np.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,\n", |
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" 2.827,3.465,1.65,2.904,2.42,2.94,1.3])\n" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "f39cbcd9", |
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"metadata": {}, |
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"source": [ |
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"The input to the model is represented by the train_X \n", |
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"Y_train represents the target or the truth values for the training data\n", |
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"The model will recieve train_X and make predictions on the weights\n", |
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"The difference between these predictions and the actual target values\n", |
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"train_Y will be used to update the weights and minimize the loss function." |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "ed8449c3", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Define the model to a simple linear regression with only one dense layer and\n", |
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"# no activation function for the first layer all train_X points are input\n", |
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"\n", |
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"# model = tf.keras.models.Sequential([\n", |
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"# tf.keras.layers.Dense(1, input_shape=[1])\n", |
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"#])" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "71e072b4", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# This model has 2 dense layers the first with relu activation\n", |
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"# and the 2nd layer has 1 output unit and uses the default\n", |
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"# linear activation function.\n", |
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"\n", |
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"model = tf.keras.models.Sequential([\n", |
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" tf.keras.layers.Dense(17, activation='relu',input_shape=[1]),\n", |
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" tf.keras.layers.Dense(1)\n", |
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"])" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "5fabf184", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# different optimizer methods can be enabled\n", |
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"\n", |
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"model.compile(optimizer=tf.keras.optimizers.Adam(0.01), loss='mean_squared_error')\n", |
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"#model.compile(optimizer=tf.keras.optimizers.SGD(0.01), loss='mean_squared_error')\n", |
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"#model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.01), loss='mean_squared_error')\n", |
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"#model.compile(optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.01), loss='mean_squared_error')\n", |
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"#model.compile(optimizer=tf.keras.optimizers.Ftrl(learning_rate=0.015), loss='mean_squared_error')" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "22c4124f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Train the model and access training parameters\n", |
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"history = model.fit(train_X, train_Y, epochs=60)\n", |
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"print(history.params)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "46615960", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Get the weights of the Dense layer\n", |
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"weights = model.layers[0].get_weights()\n", |
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"# Print the weight matrix and bias vector\n", |
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"print('Weight matrix shape:', weights[0].shape)\n", |
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"print('Bias vector shape:', weights[1].shape)\n", |
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"print (weights[0])" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "da12fc5b", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Plot the loss function\n", |
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"plt.plot(history.history['loss'])\n", |
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"plt.title(\"Loss Function\")\n", |
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"plt.xlabel(\"Epoch\")\n", |
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"plt.ylabel(\"Loss\")\n", |
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"plt.show()\n", |
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"\n", |
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"# Plot the input data and the predicted values\n", |
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"plt.plot(train_X, train_Y, 'ro', label=\"Original Data\")\n", |
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"plt.plot(train_X, model.predict(train_X), label=\"Predicted\")\n", |
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"plt.legend()\n", |
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"plt.show()" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "60417d5f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "Python 3 (ipykernel)", |
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"language": "python", |
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"name": "python3" |
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}, |
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"language_info": { |
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"codemirror_mode": { |
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"name": "ipython", |
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"version": 3 |
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}, |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.8.16" |
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} |
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}, |
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"nbformat": 4, |
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"nbformat_minor": 5 |
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} |
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